The transcriptomic footprint of Mytella strigata: de novo transcriptome assembly of a major invasive species.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-03 DOI:10.1038/s41597-025-04559-y
V G Vysakh, Sandhya Sukumaran, Wilson Sebastian, A Gopalakrishnan
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Abstract

Mytella strigata, a potentially invasive species native to South America, is rapidly spreading across various aquatic ecosystems around the globe, posing a threat to native mussels. This study presents the first comprehensive de novo transcriptome assembly of M. strigata. We generated 254 million reads, which were processed and assembled using the Trinity assembler, resulting in 60362 transcripts with an N50 of 1,578 bp and over 93-98% completeness, as confirmed by BUSCO analysis with multiple ortho-datasets. A number of databases were used for functional annotation, including UniProt, KEGG, Reactome, InterPro, and eggNOG. Gene Ontology and pathway analyses identified transcripts associated with key biological processes, including those associated with cell signalling, metabolism, stress responses, cancer pathways, and immune regulation. This dataset enriches the bivalve database by advancing the understanding of the adaptive success and evolutionary resilience of this invasive species. The present study provides a fundamental framework for future research on the ecological and evolutionary impacts of this invasive species.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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